Integrative Analysis and Machine Learning Based Characterization of Single Circulating Tumor Cells

Overview

Journal J Clin Med

Specialty General Medicine

Date 2020 Apr 26

PMID 32331451

Citations 18

Authors

Arvind Iyer

Krishan Gupta

Shreya Sharma

Kishore Hari

Yi Fang Lee

Neevan Ramalingam

Yoon Sim Yap

Jay West

Ali Asgar Bhagat

Balaram Vishnu Subramani

Burhanuddin Sabuwala

Tuan Zea Tan

Jean Paul Thiery

Mohit Kumar Jolly

Naveen Ramalingam

Debarka Sengupta

Affiliations

Soon will be listed here.

Abstract

We collated publicly available single-cell expression profiles of circulating tumor cells (CTCs) and showed that CTCs across cancers lie on a near-perfect continuum of epithelial to mesenchymal (EMT) transition. Integrative analysis of CTC transcriptomes also highlighted the inverse gene expression pattern between PD-L1 and MHC, which is implicated in cancer immunotherapy. We used the CTCs expression profiles in tandem with publicly available peripheral blood mononuclear cell (PBMC) transcriptomes to train a classifier that accurately recognizes CTCs of diverse phenotype. Further, we used this classifier to validate circulating breast tumor cells captured using a newly developed microfluidic system for label-free enrichment of CTCs.

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Marker-free characterization of full-length transcriptomes of single live circulating tumor cells.

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